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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Updated: May 19, 2026

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

A sample selection strategy for next-generation sequencing.

Chul Joo Kang1, Paul Marjoram

  • 1Department of Preventive Medicine, Keck School of Medicine, USC, Los Angeles, California, USA.

Genetic Epidemiology
|August 7, 2012
PubMed
Summary
This summary is machine-generated.

Selecting subsets of samples for deep sequencing is crucial due to cost. This study presents an algorithm to optimize sample selection for maximizing new polymorphic sites or improving imputation efficiency, including a variant for rare variant detection.

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Last Updated: May 19, 2026

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates large volumes of genetic data efficiently and affordably.
  • Deep resequencing of entire sample sets remains cost-prohibitive, necessitating strategic sample selection.
  • Optimizing sample selection is key for maximizing genetic discovery and downstream analysis efficiency.

Purpose of the Study:

  • To develop and present an algorithm for selecting optimal sample subsets for sequencing.
  • To address two primary goals: maximizing detection of novel polymorphic sites and enhancing genotype imputation accuracy.
  • To introduce a modified algorithm variant specifically designed for identifying rarer genetic variants.

Main Methods:

  • Algorithm development for sub-sample selection based on defined objectives.
  • Implementation of a variant algorithm focused on rare variant discovery.
  • Validation using simulated datasets and real-world data from the 1000 Genomes Project.

Main Results:

  • Demonstrated the effectiveness of the proposed algorithm in simulated data.
  • Validated the algorithm's performance using empirical data from a large-scale human genomics project.
  • Showcased the algorithm's utility in achieving targeted goals of polymorphism discovery and imputation improvement.

Conclusions:

  • The developed algorithm provides a sensible strategy for cost-effective sample selection in deep sequencing studies.
  • The algorithm effectively balances the trade-offs between discovering new genetic variations and improving downstream analyses.
  • The variant for rare variant detection offers a focused approach for identifying low-frequency polymorphisms.